Overview

Dataset statistics

Number of variables17
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory45.5 B

Variable types

DateTime1
Categorical2
Numeric14

Alerts

Mood_num is highly correlated with FriendsHigh correlation
Friends is highly correlated with Mood_numHigh correlation
Mood_num is highly correlated with FriendsHigh correlation
Friends is highly correlated with Mood_numHigh correlation
Mood_num is highly correlated with FriendsHigh correlation
Friends is highly correlated with Mood_numHigh correlation
Date is highly correlated with Mood_bin and 15 other fieldsHigh correlation
Mood_bin is highly correlated with Date and 2 other fieldsHigh correlation
Mood_num is highly correlated with Date and 2 other fieldsHigh correlation
Time is highly correlated with DateHigh correlation
Day is highly correlated with DateHigh correlation
Exercise is highly correlated with DateHigh correlation
Family is highly correlated with DateHigh correlation
Food is highly correlated with DateHigh correlation
Friends is highly correlated with Date and 2 other fieldsHigh correlation
Hobby is highly correlated with DateHigh correlation
Love is highly correlated with DateHigh correlation
NightOut is highly correlated with DateHigh correlation
Projects is highly correlated with DateHigh correlation
School is highly correlated with DateHigh correlation
SelfCare is highly correlated with DateHigh correlation
Sleep is highly correlated with DateHigh correlation
Entertainment is highly correlated with DateHigh correlation
Day is uniformly distributed Uniform
Date has unique values Unique
Mood_num has 17 (40.5%) zeros Zeros
Exercise has 33 (78.6%) zeros Zeros
Family has 32 (76.2%) zeros Zeros
Food has 37 (88.1%) zeros Zeros
Friends has 22 (52.4%) zeros Zeros
Hobby has 39 (92.9%) zeros Zeros
Love has 33 (78.6%) zeros Zeros
NightOut has 40 (95.2%) zeros Zeros
Projects has 30 (71.4%) zeros Zeros
School has 16 (38.1%) zeros Zeros
SelfCare has 39 (92.9%) zeros Zeros
Sleep has 36 (85.7%) zeros Zeros
Entertainment has 41 (97.6%) zeros Zeros

Reproduction

Analysis started2022-11-23 08:15:11.262810
Analysis finished2022-11-23 08:15:45.221300
Duration33.96 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Date
Date

HIGH CORRELATION
UNIQUE

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size464.0 B
Minimum2022-10-01 00:00:00
Maximum2022-11-11 00:00:00
2022-11-23T08:15:45.355146image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:45.576176image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)

Mood_bin
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size294.0 B
Good
25 
Bad
17 

Length

Max length4
Median length4
Mean length3.595238095
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBad
2nd rowBad
3rd rowGood
4th rowGood
5th rowBad

Common Values

ValueCountFrequency (%)
Good25
59.5%
Bad17
40.5%

Length

2022-11-23T08:15:45.799145image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-23T08:15:45.914238image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
good25
59.5%
bad17
40.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Mood_num
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5952380952
Minimum0
Maximum1
Zeros17
Zeros (%)40.5%
Negative0
Negative (%)0.0%
Memory size170.0 B
2022-11-23T08:15:46.012146image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4967957724
Coefficient of variation (CV)0.8346168977
Kurtosis-1.93238009
Mean0.5952380952
Median Absolute Deviation (MAD)0
Skewness-0.4025799153
Sum25
Variance0.2468060395
MonotonicityNot monotonic
2022-11-23T08:15:46.176922image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
125
59.5%
017
40.5%
ValueCountFrequency (%)
017
40.5%
125
59.5%
ValueCountFrequency (%)
125
59.5%
017
40.5%

Time
Real number (ℝ≥0)

HIGH CORRELATION

Distinct38
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.17738095
Minimum12.98333333
Maximum23.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-23T08:15:46.368011image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum12.98333333
5-th percentile15.73666667
Q116.07083333
median17.06666667
Q320.39166667
95-th percentile22.91416667
Maximum23.25
Range10.26666667
Interquartile range (IQR)4.320833333

Descriptive statistics

Standard deviation2.845344749
Coefficient of variation (CV)0.1565321625
Kurtosis-0.804637978
Mean18.17738095
Median Absolute Deviation (MAD)1.2
Skewness0.5515694577
Sum763.45
Variance8.09598674
MonotonicityNot monotonic
2022-11-23T08:15:46.547457image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
164
 
9.5%
16.066666672
 
4.8%
16.21
 
2.4%
18.733333331
 
2.4%
15.81
 
2.4%
15.733333331
 
2.4%
16.283333331
 
2.4%
17.816666671
 
2.4%
16.083333331
 
2.4%
15.933333331
 
2.4%
Other values (28)28
66.7%
ValueCountFrequency (%)
12.983333331
 
2.4%
13.533333331
 
2.4%
15.733333331
 
2.4%
15.81
 
2.4%
15.933333331
 
2.4%
164
9.5%
16.066666672
4.8%
16.083333331
 
2.4%
16.116666671
 
2.4%
16.21
 
2.4%
ValueCountFrequency (%)
23.251
2.4%
23.183333331
2.4%
22.916666671
2.4%
22.866666671
2.4%
22.766666671
2.4%
22.733333331
2.4%
22.716666671
2.4%
22.151
2.4%
21.751
2.4%
211
2.4%

Day
Categorical

HIGH CORRELATION
UNIFORM

Distinct7
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size526.0 B
Monday
Tuesday
Wednesday
Thursday
Friday
Other values (2)
12 

Length

Max length9
Median length7
Mean length7.142857143
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSaturday
2nd rowSunday
3rd rowMonday
4th rowTuesday
5th rowWednesday

Common Values

ValueCountFrequency (%)
Monday6
14.3%
Tuesday6
14.3%
Wednesday6
14.3%
Thursday6
14.3%
Friday6
14.3%
Saturday6
14.3%
Sunday6
14.3%

Length

2022-11-23T08:15:46.747620image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-23T08:15:46.902585image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
monday6
14.3%
tuesday6
14.3%
wednesday6
14.3%
thursday6
14.3%
friday6
14.3%
saturday6
14.3%
sunday6
14.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Exercise
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2142857143
Minimum0
Maximum1
Zeros33
Zeros (%)78.6%
Negative0
Negative (%)0.0%
Memory size170.0 B
2022-11-23T08:15:47.054599image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4152997322
Coefficient of variation (CV)1.938065417
Kurtosis0.0891996892
Mean0.2142857143
Median Absolute Deviation (MAD)0
Skewness1.444739675
Sum9
Variance0.1724738676
MonotonicityNot monotonic
2022-11-23T08:15:47.195571image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
033
78.6%
19
 
21.4%
ValueCountFrequency (%)
033
78.6%
19
 
21.4%
ValueCountFrequency (%)
19
 
21.4%
033
78.6%

Family
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2380952381
Minimum0
Maximum1
Zeros32
Zeros (%)76.2%
Negative0
Negative (%)0.0%
Memory size170.0 B
2022-11-23T08:15:47.356752image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4310805374
Coefficient of variation (CV)1.810538257
Kurtosis-0.3932451923
Mean0.2380952381
Median Absolute Deviation (MAD)0
Skewness1.275863678
Sum10
Variance0.1858304297
MonotonicityNot monotonic
2022-11-23T08:15:47.524751image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
032
76.2%
110
 
23.8%
ValueCountFrequency (%)
032
76.2%
110
 
23.8%
ValueCountFrequency (%)
110
 
23.8%
032
76.2%

Food
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.119047619
Minimum0
Maximum1
Zeros37
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size170.0 B
2022-11-23T08:15:47.673786image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3277700676
Coefficient of variation (CV)2.753268568
Kurtosis4.152848233
Mean0.119047619
Median Absolute Deviation (MAD)0
Skewness2.440735379
Sum5
Variance0.1074332172
MonotonicityNot monotonic
2022-11-23T08:15:47.823751image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
037
88.1%
15
 
11.9%
ValueCountFrequency (%)
037
88.1%
15
 
11.9%
ValueCountFrequency (%)
15
 
11.9%
037
88.1%

Friends
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4761904762
Minimum0
Maximum1
Zeros22
Zeros (%)52.4%
Negative0
Negative (%)0.0%
Memory size170.0 B
2022-11-23T08:15:47.996788image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5054867366
Coefficient of variation (CV)1.061522147
Kurtosis-2.09229021
Mean0.4761904762
Median Absolute Deviation (MAD)0
Skewness0.09891456369
Sum20
Variance0.2555168409
MonotonicityNot monotonic
2022-11-23T08:15:48.135752image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
022
52.4%
120
47.6%
ValueCountFrequency (%)
022
52.4%
120
47.6%
ValueCountFrequency (%)
120
47.6%
022
52.4%

Hobby
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07142857143
Minimum0
Maximum1
Zeros39
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size170.0 B
2022-11-23T08:15:48.315843image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.95
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2606611802
Coefficient of variation (CV)3.649256523
Kurtosis10.41577909
Mean0.07142857143
Median Absolute Deviation (MAD)0
Skewness3.452758095
Sum3
Variance0.06794425087
MonotonicityNot monotonic
2022-11-23T08:15:48.475841image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
039
92.9%
13
 
7.1%
ValueCountFrequency (%)
039
92.9%
13
 
7.1%
ValueCountFrequency (%)
13
 
7.1%
039
92.9%

Love
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2142857143
Minimum0
Maximum1
Zeros33
Zeros (%)78.6%
Negative0
Negative (%)0.0%
Memory size170.0 B
2022-11-23T08:15:48.661839image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4152997322
Coefficient of variation (CV)1.938065417
Kurtosis0.0891996892
Mean0.2142857143
Median Absolute Deviation (MAD)0
Skewness1.444739675
Sum9
Variance0.1724738676
MonotonicityNot monotonic
2022-11-23T08:15:48.829839image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
033
78.6%
19
 
21.4%
ValueCountFrequency (%)
033
78.6%
19
 
21.4%
ValueCountFrequency (%)
19
 
21.4%
033
78.6%

NightOut
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04761904762
Minimum0
Maximum1
Zeros40
Zeros (%)95.2%
Negative0
Negative (%)0.0%
Memory size170.0 B
2022-11-23T08:15:48.982886image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2155402687
Coefficient of variation (CV)4.526345643
Kurtosis18.29625
Mean0.04761904762
Median Absolute Deviation (MAD)0
Skewness4.40752907
Sum2
Variance0.04645760743
MonotonicityNot monotonic
2022-11-23T08:15:49.150881image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
040
95.2%
12
 
4.8%
ValueCountFrequency (%)
040
95.2%
12
 
4.8%
ValueCountFrequency (%)
12
 
4.8%
040
95.2%

Projects
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2857142857
Minimum0
Maximum1
Zeros30
Zeros (%)71.4%
Negative0
Negative (%)0.0%
Memory size170.0 B
2022-11-23T08:15:49.345957image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4572299569
Coefficient of variation (CV)1.600304849
Kurtosis-1.085448718
Mean0.2857142857
Median Absolute Deviation (MAD)0
Skewness0.9841874821
Sum12
Variance0.2090592334
MonotonicityNot monotonic
2022-11-23T08:15:49.529131image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
030
71.4%
112
 
28.6%
ValueCountFrequency (%)
030
71.4%
112
 
28.6%
ValueCountFrequency (%)
112
 
28.6%
030
71.4%

School
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.619047619
Minimum0
Maximum1
Zeros16
Zeros (%)38.1%
Negative0
Negative (%)0.0%
Memory size170.0 B
2022-11-23T08:15:49.685992image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.491507435
Coefficient of variation (CV)0.7939735488
Kurtosis-1.830898669
Mean0.619047619
Median Absolute Deviation (MAD)0
Skewness-0.5086393047
Sum26
Variance0.2415795587
MonotonicityNot monotonic
2022-11-23T08:15:49.852987image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
126
61.9%
016
38.1%
ValueCountFrequency (%)
016
38.1%
126
61.9%
ValueCountFrequency (%)
126
61.9%
016
38.1%

SelfCare
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07142857143
Minimum0
Maximum1
Zeros39
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size170.0 B
2022-11-23T08:15:49.999629image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.95
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2606611802
Coefficient of variation (CV)3.649256523
Kurtosis10.41577909
Mean0.07142857143
Median Absolute Deviation (MAD)0
Skewness3.452758095
Sum3
Variance0.06794425087
MonotonicityNot monotonic
2022-11-23T08:15:50.157668image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
039
92.9%
13
 
7.1%
ValueCountFrequency (%)
039
92.9%
13
 
7.1%
ValueCountFrequency (%)
13
 
7.1%
039
92.9%

Sleep
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1428571429
Minimum0
Maximum1
Zeros36
Zeros (%)85.7%
Negative0
Negative (%)0.0%
Memory size170.0 B
2022-11-23T08:15:50.328630image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3541688017
Coefficient of variation (CV)2.479181612
Kurtosis2.606303419
Mean0.1428571429
Median Absolute Deviation (MAD)0
Skewness2.117634293
Sum6
Variance0.1254355401
MonotonicityNot monotonic
2022-11-23T08:15:50.518421image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
036
85.7%
16
 
14.3%
ValueCountFrequency (%)
036
85.7%
16
 
14.3%
ValueCountFrequency (%)
16
 
14.3%
036
85.7%

Entertainment
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02380952381
Minimum0
Maximum1
Zeros41
Zeros (%)97.6%
Negative0
Negative (%)0.0%
Memory size170.0 B
2022-11-23T08:15:50.701928image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.15430335
Coefficient of variation (CV)6.480740698
Kurtosis42
Mean0.02380952381
Median Absolute Deviation (MAD)0
Skewness6.480740698
Sum1
Variance0.02380952381
MonotonicityNot monotonic
2022-11-23T08:15:50.870500image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
041
97.6%
11
 
2.4%
ValueCountFrequency (%)
041
97.6%
11
 
2.4%
ValueCountFrequency (%)
11
 
2.4%
041
97.6%

Interactions

2022-11-23T08:15:41.938032image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:11.864835image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:13.808287image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:15.831359image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:17.781200image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:19.778633image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:22.250015image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:24.664876image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:27.155838image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:29.545303image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:32.186671image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:34.691537image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:37.021178image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:39.350219image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:42.112790image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:12.012254image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:13.953290image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:15.983428image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:17.907236image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:19.925604image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:22.417022image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:24.853919image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:27.319897image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:29.705328image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:32.349716image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:34.848536image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:37.186188image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:39.513369image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:42.271789image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:12.140927image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:14.072284image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:16.119421image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:18.081202image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:20.060603image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:22.580047image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:25.023919image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:27.515898image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:29.844297image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:32.546717image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:35.016577image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:37.352148image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:39.668369image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:42.428794image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:12.280056image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:14.211287image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:16.261621image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:18.220201image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:20.206635image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:22.751046image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:25.191967image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:27.682928image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:30.298831image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:32.757744image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:35.193120image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:37.516720image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:39.807530image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:42.583790image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:12.418984image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:14.344316image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:16.392586image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:18.351204image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:20.346636image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:22.920123image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:25.393239image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:27.832896image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:30.498995image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:32.956313image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:35.361117image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:37.683719image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:39.958528image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:42.742823image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:12.553967image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:14.482284image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:16.537617image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:18.484200image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:20.503680image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:23.120122image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:25.599090image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:27.999899image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:30.670013image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:33.134689image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:35.517338image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:37.837383image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:40.110526image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:42.923948image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:12.692067image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:14.616284image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:16.669582image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:18.625202image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:20.655740image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:23.293122image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:25.782130image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:28.153107image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:30.837577image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:33.295688image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:35.684116image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:38.011372image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:40.268527image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:43.098949image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:12.841784image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:14.745285image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:16.798581image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:18.766668image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:20.794779image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:23.455120image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:25.941128image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:28.347052image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:31.003547image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:33.484259image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:35.855116image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:38.188532image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:40.768828image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:43.264948image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:12.976787image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:14.875288image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:16.923180image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:18.903569image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:20.969921image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:23.625961image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:26.124130image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:28.536226image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:31.152376image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:33.651388image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:36.030995image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:38.369409image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:40.930625image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:43.417055image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:13.121847image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:15.011322image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:17.061141image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:19.043570image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:21.358020image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:23.795746image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:26.282855image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:28.702226image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:31.317356image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:33.831375image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:36.193025image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:38.545469image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:41.083621image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:43.568949image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:13.259387image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:15.147368image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:17.189234image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:19.187604image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:21.590016image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:23.956743image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:26.453976image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:28.858301image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:31.486328image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:34.007506image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:36.366032image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:38.717028image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:41.255621image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:43.743969image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:13.397475image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:15.289341image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:17.371199image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:19.334601image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:21.747158image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:24.125874image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:26.648839image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:29.026306image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:31.667547image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:34.184473image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:36.537029image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:38.887086image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:41.426660image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:43.903970image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:13.530998image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:15.563339image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:17.505201image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:19.483599image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:21.919021image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:24.288878image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:26.822837image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:29.230298image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:31.847604image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:34.342535image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:36.715038image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:39.038323image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:41.599762image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:44.043967image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:13.670128image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:15.697347image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:17.644200image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:19.629630image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:22.066018image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:24.458875image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:26.979841image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:29.388299image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:32.020543image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:34.517570image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:36.874178image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:39.191056image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:15:41.755658image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2022-11-23T08:15:51.086774image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-23T08:15:51.410041image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-23T08:15:51.738046image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-23T08:15:52.046188image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-23T08:15:52.245407image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-23T08:15:44.356968image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-23T08:15:45.033268image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DateMood_binMood_numTimeDayExerciseFamilyFoodFriendsHobbyLoveNightOutProjectsSchoolSelfCareSleepEntertainment
02022-10-01Bad016.200000Saturday000000001000
12022-10-02Bad020.750000Sunday010000000000
22022-10-03Good118.350000Monday111111011110
32022-10-04Good122.733333Tuesday000001000100
42022-10-05Bad022.150000Wednesday000000001000
52022-10-06Bad016.350000Thursday000000001000
62022-10-07Good117.316667Friday010100001000
72022-10-08Good118.866667Saturday010000000100
82022-10-09Bad022.916667Sunday000001000001
92022-10-10Bad012.983333Monday000001000000

Last rows

DateMood_binMood_numTimeDayExerciseFamilyFoodFriendsHobbyLoveNightOutProjectsSchoolSelfCareSleepEntertainment
322022-11-02Bad016.816667Wednesday000000001000
332022-11-03Good118.833333Thursday110101001000
342022-11-04Good116.000000Friday110101001000
352022-11-05Bad013.533333Saturday000001000000
362022-11-06Bad016.000000Sunday001001000000
372022-11-07Bad016.400000Monday000000001010
382022-11-08Good116.116667Tuesday000100011000
392022-11-09Good121.750000Wednesday100100011000
402022-11-10Good117.783333Thursday000100010000
412022-11-11Good116.550000Friday000001000000